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August 4, 2009

Getting Benefits from Custom Relationship Management

Filed under: Data Integration, Data Migration, Data Warehousing — Tags: , — Olga Belokurskaya @ 5:33 am

Customer relationship management (CRM) systems prove to be beneficial. They are widely been implemented and are regarded a powerful solution that improve provider-customer relations and help companies become more customer-centric.

Here are some factors to consider for getting benefits from CRM integration:

  • System integration provider choice. – If you are not sure you staff is skilled enough for CRM implementation or integration work, you better choose professionals to do this for you. They may help analyze what functionality is more advantageous for company’s certain business needs. Thus it will help avoid acquiring bulky solutions, many parts of which will never be in use.
  • End user adoption. – This factor is critical in the success of a CRM implementation, however ignore it. According to Forrester research, companies that achieved only 30% user adoption after three years saw only 5% ROI on their CRM projects. A lot of organizations don’t think about the right user training. Companies looking gain maximum benefits from their CRM investments should focus on additional user training.
  • Real time applications. – Access to real-time information is highly important. Make sure you select applications that perform live data exchange instead of batch processing.
  • System usage monitoring. – Consider monitoring usage of the system, either by soliciting user feedback, leveraging system monitoring tools included in many CRM applications, or investing in monitoring software.

June 30, 2009

Customer Data Integration vs Data Warehouse: the Difference

Filed under: Data Integration, Data Warehousing — Tags: , — Olga Belokurskaya @ 11:51 pm

The extensive world of enterprise data is quite tricky; however, it’s very important to clearly distinguish all the solutions used when dealing with data.

As customers are very important for any company, customer data is among the greatest company’s assets. In fact, customer data integration (CDI) solutions are to deliver the fullest information about customers.

However, as CDI is a software solution, and, in fact, is a clearinghouse for data synchronization and deployment, it is inevitably compared to data warehouses. There are several reasons of this confusion:

  • the aim of both solutions is to accommodate clean, meaningful information to the enterprise
  • both solutions are undoubtedly beneficial for business
  • both demand a solid partnership of business and IT

This confusion is risky, for these solutions differ in term of their positioning as well as in term of their usage. According to Jill Dyché one of the most acknowledged BI experts:

Data warehouses are designed and built to support business intelligence, and are meant for use by business people. Best practice data warehouses are those that have been planned around a set of business requirements that inform a series of applications—we call this the BI Portfolio—that are deployed incrementally to the business over time.

CDI, however, is purpose-built for operational data integration. The CDI hub is the ultimate home of customer master data that has been matched, reconciled, and certified, and is available to a series of business applications and systems (not end users). Unlike the data warehouse, which usually stores historical detail, summarized data, and time-variant information, the CDI hub stores or points to certified master data about a customer.

June 25, 2009

Open Source Data Warehouses: the Benefits

Filed under: Data Warehousing, Database Integration, Open Source — Tags: , — Olga Belokurskaya @ 1:21 am

Open source data warehouses possess the same options as any other types of open source software: the same model of licensing, community development processes, and same degree of openness. They may be offered as free downloads, or for a nominal flat fee, as fully supported systems. Or there may be no limit to the number of licenses and implementations a company may make with the software.

Acording to BeyeNetwork article, the benefits of the open source data warehouses are following:

  • Up front and maintenance costs are less than those of proprietary software. Besides, there is a possibility to customize the products companies use to improve their operations, for the original source code is open and may be downloaded.
  • Skill sets that are widely available in the market are employed.  As a result, an organization with existing database or data warehouse expertise will not have to look further when a new open source data warehouse project is put into place.
  • Improved standardization. Transparent and community supported open source code considers important standards to be consistently supported across all versions and implementations. Something that proprietary formats cannot and will not offer.
  • Flexibility which enables enterprises to expand the solutions to an unlimited number of users, with no per-user or per-processor charges of proprietary software packages.
  • Community effect. Open source solutions leverage communities of developers and innovators to advance development. New code and features are contributed back to the community, constantly increasing the range of new options available to end users.  Moreover, companies may address the community in order to fix any bugs or security flaws, which takes, normally, only days, instead of waiting weeks and months for the next security patch or service pack from a vendor.
  • Incremental implementation.  There is no need to a mega project at once. Projects can start small and build upon the success of implementations. This dumps the tendency to “overpromise,” which is often a necessary evil for acquiring optimal levels of funding for data warehouse projects.

June 24, 2009

Getting Most out of Open Source Data Warehouse

Filed under: Data Warehousing, Open Source — Tags: — Olga Belokurskaya @ 5:53 am

There’s been quite a period of time since open source data warehouses evolved and gained popularity. However, an open source data warehouse is still regarded as a solution for small or mid-sized companies lacking enough budgets for solid proprietary solutions. Bigger companies may also use open source solutions as complimentary to their proprietary data warehouses.
Getting the most out of an open source data warehouse implementation is possible. There are some ways below provided by Claudia Imhoff:

  • Open source data warehouses complementing already existing proprietary enterprise solutions may help quickly address the new company’s needs. Proprietary solutions being more strategic are not so fast to react to those changes.
  • Normally, it’s the analysts who work with data warehouses; they are familiar with building massive queries and other technical stuff. But in some cases, there are end users who don’t have special technical knowledge, and need as much ease of use as possible.
  • Open source data warehouses should be compatible with related open source environments.
  • While open source data warehouses may seem cheaper than proprietary solutions at first, additional costs, such as transition and training costs, should be taken into account.

April 25, 2009

Data Warehousing Pros and Cons

Filed under: Data Warehousing — Tags: , — Olga Belokurskaya @ 4:32 am

First, let’s remember what is data warehouse, and why it may be useful for a business.

In fact, it is a repository of an organization’s  data which is electronically stored, and it is designed to facilitate reporting and analysis. The broader meaning of data warehouse focuses not only on data storage, but the means to retrieve and analyze data, to extract, transform and load data, and to manage the data dictionary are also considered essential components of a data warehousing system.

Nowadays, data warehousing is a popular management technique and is frequently used as a business model. However, not every system is applicable to every business setting. So when thinking about implementing the strategy, one should consider pros and cons of data warehousing.

Among the major benefits of data warehousing is enhanced access to data and information and easy reporting and analysis. Besides:

  • Data retrieval is faster within data warehouses.
  • Prior to loading data into the data warehouse, inconsistencies are identified and resolved.
  • Data warehouses can work in conjunction with and, hence, enhance the value of operational business applications, such as, for example, CRM systems.

And here are some cons:

  • Preparation is very frequently time consuming for effort is needed to create a cohesive, compatible system of data collection, storage, and retrieval. Moreover, because data must be extracted, transformed and loaded into the warehouse, there is an element of latency in data warehouse data.
  • Compatibility with existing systems. The use of data warehousing technology may require a company to modify the database system already in place. This could really be the foremost concern of businesses when adapting the model given the cost of the computer systems and software needed.
  • Security flaws that data warehousing technology may contain. If the database contains sensitive information, its use may be restricted to a limited group of people and precautions will be required to insure that access is not compromised. Limited data access situations can also effect the overall utilization of the data strategy.
  • Over their life, data warehouses can have high costs. The data warehouse is usually not static, it gets outdated and needs regular maintenance, which may be quite costly.

So, before any implementations, one should make sure that data warehousing will be a good fit for the business and be prepared to commit to the level of work required to get the system in place. However, once data warehouse starts working, most companies are glad to have their “corporate memory.”

April 2, 2009

Integrating New Systems Acquired With the Merger in a Data Warehouse

We all remember those talks about mergers and acquisitions among companies due to the present economic downturn.  Integrating new systems acquired with the merger or acquisition in a data warehouse is a big challenge for those in charge. There are several things with respect to ETL integration from one system to the other one can do to make this process easier. Here is an advice by Joe Oates, an internationally known consultant on data warehousing:

  • Get involved as early as possible. Work out an arrangement with IT management so that the data warehouse team can be involved with the planning for the merger or acquisition from the start.
  • In your requirements gathering, you should have obtained a list of the top 20 questions that each manager or involved stakeholder in the current data warehouse project would like to get from the data warehouse. Consolidate these questions into a single list and run them by the management of the new company as well as your existing management to see if they still apply and if any new questions are needed. This is something you should do periodically.
  • Prepare templates for the ETL process flow. You may not have much time to bring the new systems in. You also may have to bring on inexperienced resources to do the ETL. Having well-designed templates that handle the overall flow of the ETL, the dimension processing, fact processing, exception processing and audit trail will be invaluable. The best source for this information in a readily available book that I have seen is The Microsoft Data Warehouse Toolkit: With SQL Server 2005 and the Microsoft Business Intelligence Toolset by Ralph Kimball, et al. Regardless of the database system or ETL tool that you may use, the section on how to develop the ETL is excellent and the principles can be transferred to any ETL tool or database.
  • You will probably want to have two or more phases involved in bringing the data over from the new company. It is much easier to handle several smaller ETL projects than one gigantic ETL project. Regardless of whether you can or not, the previously mentioned templates will make your life much easier.
  • Negotiate a trial period or parallel if the acquired company already has a DW. You may have to develop special reports and/or analyses for the new company. Being able to work out the details before going into full production will help you set expectations and help ensure a smoother transition.

March 23, 2009

Avoiding Common Data Quality Management Mistakes

Filed under: Data Quality, Data Warehousing — Tags: — Olga Belokurskaya @ 7:22 am

Implementing data quality management programs is an important step for any organization. Due to the size and complexity of an organization data quality management can be quite complicated and, sometimes, may turn (and very often it does) into a nightmare. There are three most common mistakes that companies make when starting data quality management project:

  1. Hope for a “magic tool” – meaning that too often organizations believe that a packaged solution can “fix” noncompliant data though in the first place they should care about eliminating the introduction of bad data. Although data quality tools are critical components of a data quality program, one must first question the motivation for purchasing a tool, then the process itself, and consider the improvement potential in terms of contributing to the effectiveness of the program.
  2. Lack of the right expertise – which means that the success in developing a data quality management program depends on having both business and technical expertise but, in fact, lots of organizations disregard this crucial moment, being sure that as soon as a data quality program is initiated within an organization, there should be some visible improvement to the data.  This is definitely wrong. Moreover, the data quality manager is often viewed as having responsibility for some data quality improvement action without necessarily having either the knowledge or authority to make it happen, and the team has no idea where to begin. This is the result of not bringing in the proper expertise to help get the program off the ground.
  3. Not accounting for organizational culture changes – no technology in the world will eliminate data quality problems, without understanding of how people’s behavior allows the introduction of information flaws in the first place. Without the cooperation of upstream systems owners, data warehousing managers are often helpless to control the quality of incoming data. Stricter data quality needs at the data warehouse demand resource allocation by upstream managers.

Here are the solutions on how to avoid data quality management mistakes, provided by David Loshin, an expert in information management:

  • Exploit the advisory role of data quality teams and use internal procedures to attach responsibility and accountability for data quality improvement to the existing information management authority.
  • Don’t forget training in the use of policies and procedures — especially in the use of acquired tools.
  • Hire professionals with experience in managing data quality projects and programs from the start. These individuals will be able to identify opportunities for tactical successes that together contribute to the strategic success of the program.
  • Engage external experts to help jump-start the improvement process. This will reassure your team that your problems are not unique and will allow you to learn from others’ best practices.

January 8, 2009

Business Intelligence 2009 Predictions

Filed under: Data Integration, Data Mashup, Data Warehousing — Alena Semeshko @ 4:29 am

Jeff Kelly from SearchDataManagement.com has gathered and posted various forecasts as to what awaits Business Intelligence industry in 2009. The analysts and experts sharing their predictions were: Wayne Eckerson, Director of research and services for the Data Warehousing Institute, James G. Kobielus, Senior analyst at Cambridge-based Forrester Research covering BI and data warehousing, and various analysts at Gartner Inc.

A few of my highlights from the article:

Open Source BI will gain even more popularity with companies looking to reduce costs. In turn, open source BI tools will be developing rapidly and should win a greater portion of deals in 2009.

Software as a Service (SaaS) will flourish in the midmarket (too, thanks to the challenging economic situation), particularly in companies that consider IT resources redundant.

For the same reason, hosted and subscription-based services will largely replace premises-based tools and platforms.

In a soft economy, any on-demand pay-as-you-go offering becomes more attractive across all customer segments. Just as important, the increasing scalability, performance, flexibility, and availability demands on the enterprise BI infrastructure are spurring many users to consider outsourced offerings.

Federated data environments will be adopted as the means to cure the problems with decentralized information scattered throughout the enterprise. IT organizations, in turn, will supplement their enterprise data warehouses by beefing up their enterprise information integration middleware and semantic virtualization layers.

Gartner, as always, is more number-oriented in its predictions: the company’s analysts predict that the lack of sufficient the information, processes and tools, along with under-investment in information infrastructure and business users’ tools will result in more than 35% of the top 5,000 global companies regularly failing to make insightful decisions about significant changes in their business and markets through 2012.

Another interesting estimation by Gartner is that by 2012, business units will control at least 40% of the total budget for BI, meaning that spending on CRM (corporate performance management), online marketing analytics, predictive analytics, and other packaged applications will only go up.

Finally, mashups. Easy and cost-effective as they are, mashups will go mainstream with BI-integrated tools leading the way.

Gartner:

By 2012, one-third of analytic applications applied to business processes will be delivered through large-grained application mashups.

January 5, 2009

EII plus Data Warehousing

Filed under: Data Warehousing, EAI — Alena Semeshko @ 3:08 am

The Holiday season is over and as much as I hate to admit it, life is getting back to the routine.
Oh well I was going to share a few thoughts on integration before disappearing for a long weekend, but didn’t get to it in the holiday rush, so might as well do it now.

Enterprise information integration (EII), another technology that seems to be neglected in the SOA, mashup, and warehousing craze of the day.

It’s integration, however, that really expands data throughout the company. When I tried looking deeper into this concept, I came across this article on how the combination of data warehousing and EII working together could really benefit the company.

With the ability to integrate data from multiple data sources that EII tools provide, and the scalability of warehousing applications, you could work wonders. Well, see the article for yourself for more details.

November 21, 2008

A Single View of Enterprise Data Management

I just stumbled upon this article by Ajay Bhargava on BeyeNetworks. The article explores the recent trend of closer alliance between enterprise data management (EDM) and data warehousing/business intelligence. I quite liked the way Mr. Bhargava doesn’t leave out such fundamental components of enterprise data as data quality, metadata, data security, data governance, etc.

Here’s his vision of the key component that make up EDM and the fields that benefit from them:

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